Data Completeness & Data Consistency for Covid 19 Dataset
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Load your dataset into a DataFrame
df1 = pd.read_csv('COVID_19_Hospitalization_Trends_Report_Data_file_20230602.csv')
# 1. Data Completeness - Missing Values
missing_percentage = (df1.isnull().sum() / len(df1)) * 100
print("Data Completeness - Missing Values:")
print(missing_percentage)
# 2. Data Consistency - Duplicate Records
duplicates = df1[df1.duplicated()]
duplicate_percentage = (len(duplicates) / len(df1)) * 100
print("\nData Consistency - Duplicate Records:")
print(f"Number of Duplicate Records: {len(duplicates)}")
print(f"Percentage of Duplicate Records: {duplicate_percentage}%")
# 1. Data Completeness - Missing Values
missing_percentage = (df1.isnull().sum() / len(df1)) * 100
# Visualize missing values using a bar plot
plt.figure(figsize=(10, 6))
sns.barplot(x=missing_percentage.index, y=missing_percentage.values)
plt.xticks(rotation=90)
plt.title('Percentage of Missing Values by Attribute')
plt.xlabel('Attribute')
plt.ylabel('Percentage Missing')
plt.show()
# 2. Data Consistency - Duplicate Records
duplicates = df1[df1.duplicated()]
duplicate_percentage = (len(duplicates) / len(df1)) * 100
# Visualize duplicate records
plt.figure(figsize=(6, 4))
sns.countplot(data=duplicates)
plt.xticks(rotation=90)
plt.title('Duplicate Records Count')
plt.xlabel('Number of Duplicates')
plt.ylabel('Frequency')
plt.show()
Data Completeness - Missing Values: Year 0.000000 Month 0.000000 Bene_Geo_Desc 0.000000 Bene_Mdcd_Mdcr_Enrl_Stus 0.000000 Bene_Race_Desc 0.000000 Bene_Sex_Desc 0.000000 Bene_Mdcr_Entlmt_Stus 0.000000 Bene_Age_Desc 0.000000 Bene_RUCA_Desc 0.000000 Total_Hosp 19.713975 Total_Enrl 0.313301 Total_Hosp_Per100K 19.778478 Avg_LOS 25.445532 Pct_Dschrg_SNF 25.445532 Pct_Dschrg_Expired 25.445532 Pct_Dschrg_Home 25.445532 Pct_Dschrg_Hspc 25.445532 Pct_Dschrg_HomeHealth 25.445532 Pct_Dschrg_Other 25.445532 dtype: float64 Data Consistency - Duplicate Records: Number of Duplicate Records: 0 Percentage of Duplicate Records: 0.0%
Quality Score for Data Completeness and Data Consistency
# 1. Data Completeness - Missing Values
missing_percentage = (df1.isnull().sum() / len(df1)) * 100
data_completeness_score = 100 - missing_percentage.mean()
# 2. Data Consistency - Duplicate Records
duplicates = df1[df1.duplicated()]
duplicate_percentage = (len(duplicates) / len(df1)) * 100
data_consistency_score = 100 - duplicate_percentage
# Calculate an overall quality score
quality_score = (data_completeness_score + data_consistency_score) / 2
print("Data Completeness - Missing Values:")
print(missing_percentage)
print(f"Data Completeness Score: {data_completeness_score}%")
print("\nData Consistency - Duplicate Records:")
print(f"Number of Duplicate Records: {len(duplicates)}")
print(f"Percentage of Duplicate Records: {duplicate_percentage}%")
print(f"Data Consistency Score: {data_consistency_score}%")
print(f"Overall Quality Score: {quality_score}%")
Data Completeness - Missing Values: Year 0.000000 Month 0.000000 Bene_Geo_Desc 0.000000 Bene_Mdcd_Mdcr_Enrl_Stus 0.000000 Bene_Race_Desc 0.000000 Bene_Sex_Desc 0.000000 Bene_Mdcr_Entlmt_Stus 0.000000 Bene_Age_Desc 0.000000 Bene_RUCA_Desc 0.000000 Total_Hosp 19.713975 Total_Enrl 0.313301 Total_Hosp_Per100K 19.778478 Avg_LOS 25.445532 Pct_Dschrg_SNF 25.445532 Pct_Dschrg_Expired 25.445532 Pct_Dschrg_Home 25.445532 Pct_Dschrg_Hspc 25.445532 Pct_Dschrg_HomeHealth 25.445532 Pct_Dschrg_Other 25.445532 dtype: float64 Data Completeness Score: 88.53029072931125% Data Consistency - Duplicate Records: Number of Duplicate Records: 0 Percentage of Duplicate Records: 0.0% Data Consistency Score: 100.0% Overall Quality Score: 94.26514536465562%
Data Completeness & Data Consistency for Faclevel Dataset
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Load your dataset into a DataFrame
df2 = pd.read_csv('faclevel_06.07.2020.csv')
# 1. Data Completeness - Missing Values
missing_percentage = (df2.isnull().sum() / len(df2)) * 100
print("Data Completeness - Missing Values:")
print(missing_percentage)
# 2. Data Consistency - Duplicate Records
duplicates = df2[df2.duplicated()]
duplicate_percentage = (len(duplicates) / len(df2)) * 100
print("\nData Consistency - Duplicate Records:")
print(f"Number of Duplicate Records: {len(duplicates)}")
print(f"Percentage of Duplicate Records: {duplicate_percentage}%")
# 1. Data Completeness - Missing Values
missing_percentage = (df2.isnull().sum() / len(df2)) * 100
# Visualize missing values using a bar plot
plt.figure(figsize=(10, 6))
sns.barplot(x=missing_percentage.index, y=missing_percentage.values)
plt.xticks(rotation=90)
plt.title('Percentage of Missing Values by Attribute')
plt.xlabel('Attribute')
plt.ylabel('Percentage Missing')
plt.show()
# 2. Data Consistency - Duplicate Records
duplicates = df2[df2.duplicated()]
duplicate_percentage = (len(duplicates) / len(df2)) * 100
# Visualize duplicate records
plt.figure(figsize=(6, 4))
sns.countplot(data=duplicates)
plt.xticks(rotation=90)
plt.title('Duplicate Records Count')
plt.xlabel('Number of Duplicates')
plt.ylabel('Frequency')
plt.show()
Data Completeness - Missing Values: Week Ending 0.000000 Federal Provider Number 0.000000 Provider Name 0.017300 Provider Address 0.017300 Provider City 0.017300 Provider State 0.017300 Provider Zip Code 0.017300 Submitted Data 0.000000 Passed Quality Assurance Check 7.064700 Residents Weekly Admissions COVID-19 7.393392 Residents Total Admissions COVID-19 7.393392 Residents Weekly Confirmed COVID-19 7.393392 Residents Total Confirmed COVID-19 7.393392 Residents Weekly Suspected COVID-19 7.393392 Residents Total Suspected COVID-19 7.393392 Residents Weekly All Deaths 7.393392 Residents Total All Deaths 7.393392 Residents Weekly COVID-19 Deaths 7.393392 Residents Total COVID-19 Deaths 7.393392 Number of All Beds 7.838855 Total Number of Occupied Beds 7.590174 Resident Access to Testing in Facility 8.072399 Laboratory Type Is State Health Dept 8.072399 Laboratory Type Is Private Lab 8.072399 Laboratory Type Is Other 8.072399 Staff Weekly Confirmed COVID-19 7.393392 Staff Total Confirmed COVID-19 7.393392 Staff Weekly Suspected COVID-19 7.393392 Staff Total Suspected COVID-19 7.393392 Staff Weekly COVID-19 Deaths 7.393392 Staff Total COVID-19 Deaths 7.393392 Shortage of Nursing Staff 8.680045 Shortage of Clinical Staff 8.682207 Shortage of Aides 8.680045 Shortage of Other Staff 8.682207 Any Current Supply of N95 Masks 8.755730 One-Week Supply of N95 Masks 8.760055 Any Current Supply of Surgical Masks 8.760055 One-Week Supply of Surgical Masks 8.760055 Any Current Supply of Eye Protection 8.760055 One-Week Supply of Eye Protection 8.760055 Any Current Supply of Gowns 8.760055 One-Week Supply of Gowns 8.762218 Any Current Supply of Gloves 8.760055 One-Week Supply of Gloves 8.760055 Any Current Supply of Hand Sanitizer 8.766543 One-Week Supply of Hand Sanitizer 8.770868 Ventilator Dependent Unit 10.455410 Number of Ventilators in Facility 96.609290 Number of Ventilators in Use for COVID-19 96.609290 Any Current Supply of Ventilator Supplies 96.611452 One-Week Supply of Ventilator Supplies 96.609290 Total Resident Confirmed COVID-19 Cases Per 1,000 Residents 7.825880 Total Resident COVID-19 Deaths Per 1,000 Residents 7.825880 Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases 72.212611 dtype: float64 Data Consistency - Duplicate Records: Number of Duplicate Records: 0 Percentage of Duplicate Records: 0.0%
Quality score for data completenss and data consistency
# 1. Data Completeness - Missing Values
missing_percentage = (df2.isnull().sum() / len(df2)) * 100
data_completeness_score = 100 - missing_percentage.mean()
# 2. Data Consistency - Duplicate Records
duplicates = df2[df2.duplicated()]
duplicate_percentage = (len(duplicates) / len(df2)) * 100
data_consistency_score = 100 - duplicate_percentage
# Calculate overall data quality score
overall_quality_score = (data_completeness_score + data_consistency_score) / 2
print("Data Quality Scores:")
print(f"Data Completeness Score: {data_completeness_score}")
print(f"Data Consistency Score: {data_consistency_score}")
print(f"Overall Data Quality Score: {overall_quality_score}")
Data Quality Scores: Data Completeness Score: 85.49484552295728 Data Consistency Score: 100.0 Overall Data Quality Score: 92.74742276147865
Outliers for Covid 19 Dataset
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Specify the columns to analyze for outliers
columns_to_check = ['Total_Hosp', 'Total_Enrl', 'Total_Hosp_Per100K', 'Avg_LOS',
'Pct_Dschrg_SNF', 'Pct_Dschrg_Expired', 'Pct_Dschrg_Home',
'Pct_Dschrg_Hspc', 'Pct_Dschrg_HomeHealth', 'Pct_Dschrg_Other']
# Create a figure to display the data and outliers
plt.figure(figsize=(16, 10))
# Loop through the specified columns
for i, column_name in enumerate(columns_to_check, 1):
plt.subplot(2, 5, i)
# Create a boxplot to visualize the distribution
sns.boxplot(data=df1, y=column_name)
plt.title(f'Boxplot for {column_name}')
# Calculate the Interquartile Range (IQR)
Q1 = df1[column_name].quantile(0.25)
Q3 = df1[column_name].quantile(0.75)
IQR = Q3 - Q1
# Define the lower and upper bounds to identify outliers
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
# Identify outliers
outliers = df1[(df1[column_name] < lower_bound) | (df1[column_name] > upper_bound)]
# Plot the outliers as red points on the boxplot
sns.scatterplot(data=outliers, x=outliers.index, y=column_name, color='red', label='Outliers')
# Adjust layout
plt.tight_layout()
# Show the plot
plt.show()
# Print the identified outliers for each column
for column_name in columns_to_check:
print(f'Outliers in {column_name}:')
print(outliers)
Outliers in Total_Hosp:
Year Month Bene_Geo_Desc Bene_Mdcd_Mdcr_Enrl_Stus \
1259 2020 1 National All
1359 2020 2 National All
1404 2020 2 National All
1432 2020 2 National All
1478 2020 3 National All
... ... ... ... ...
53215 2022 Second half North Dakota All
53411 2022 Second half Utah All
53438 2022 Second half Vermont All
53440 2022 Second half Vermont All
53493 2022 Second half West Virginia All
Bene_Race_Desc Bene_Sex_Desc Bene_Mdcr_Entlmt_Stus \
1259 Hispanic All Disabled
1359 Asian/Pacific Islander Male All
1404 Asian/Pacific Islander All All
1432 Black/African American All All
1478 American Indian/Alaska Native Male All
... ... ... ...
53215 All All ESRD
53411 Black/African American All All
53438 All All Disabled
53440 All All All
53493 Hispanic All All
Bene_Age_Desc Bene_RUCA_Desc Total_Hosp Total_Enrl \
1259 All All 45.0 958664.0
1359 All All 26.0 952855.0
1404 65-74 All 19.0 1147659.0
1432 All Rural 18.0 747724.0
1478 All All 61.0 116532.0
... ... ... ... ...
53215 All All 35.0 5029.0
53411 All All 19.0 17552.0
53438 All All 99.0 119506.0
53440 0-64 All 102.0 120583.0
53493 All All 16.0 15140.0
Total_Hosp_Per100K Avg_LOS Pct_Dschrg_SNF Pct_Dschrg_Expired \
1259 4.6940 15.2000 0.0222 0.0667
1359 2.7286 16.8846 0.0769 0.2308
1404 1.6555 28.9474 0.0526 0.0526
1432 2.4073 64.1667 0.1111 0.0000
1478 52.3461 17.0000 0.0820 0.4426
... ... ... ... ...
53215 695.9634 9.0000 0.1143 0.0857
53411 108.2498 6.4211 0.1579 0.0000
53438 82.8410 20.5556 0.0707 0.0606
53440 84.5890 20.2549 0.0784 0.0588
53493 105.6803 5.8750 0.0000 0.0625
Pct_Dschrg_Home Pct_Dschrg_Hspc Pct_Dschrg_HomeHealth \
1259 0.6000 0.0000 0.1556
1359 0.3846 0.0000 0.1154
1404 0.6316 0.0000 0.0526
1432 0.4444 0.0000 0.2222
1478 0.3115 0.0000 0.0164
... ... ... ...
53215 0.5143 0.0286 0.0857
53411 0.2105 0.1579 0.3158
53438 0.5354 0.0101 0.1717
53440 0.5294 0.0098 0.1765
53493 0.5000 0.0625 0.1875
Pct_Dschrg_Other
1259 0.1556
1359 0.1923
1404 0.2105
1432 0.2222
1478 0.1475
... ...
53215 0.1714
53411 0.1579
53438 0.1515
53440 0.1471
53493 0.1875
[1357 rows x 19 columns]
Outliers in Total_Enrl:
Year Month Bene_Geo_Desc Bene_Mdcd_Mdcr_Enrl_Stus \
1259 2020 1 National All
1359 2020 2 National All
1404 2020 2 National All
1432 2020 2 National All
1478 2020 3 National All
... ... ... ... ...
53215 2022 Second half North Dakota All
53411 2022 Second half Utah All
53438 2022 Second half Vermont All
53440 2022 Second half Vermont All
53493 2022 Second half West Virginia All
Bene_Race_Desc Bene_Sex_Desc Bene_Mdcr_Entlmt_Stus \
1259 Hispanic All Disabled
1359 Asian/Pacific Islander Male All
1404 Asian/Pacific Islander All All
1432 Black/African American All All
1478 American Indian/Alaska Native Male All
... ... ... ...
53215 All All ESRD
53411 Black/African American All All
53438 All All Disabled
53440 All All All
53493 Hispanic All All
Bene_Age_Desc Bene_RUCA_Desc Total_Hosp Total_Enrl \
1259 All All 45.0 958664.0
1359 All All 26.0 952855.0
1404 65-74 All 19.0 1147659.0
1432 All Rural 18.0 747724.0
1478 All All 61.0 116532.0
... ... ... ... ...
53215 All All 35.0 5029.0
53411 All All 19.0 17552.0
53438 All All 99.0 119506.0
53440 0-64 All 102.0 120583.0
53493 All All 16.0 15140.0
Total_Hosp_Per100K Avg_LOS Pct_Dschrg_SNF Pct_Dschrg_Expired \
1259 4.6940 15.2000 0.0222 0.0667
1359 2.7286 16.8846 0.0769 0.2308
1404 1.6555 28.9474 0.0526 0.0526
1432 2.4073 64.1667 0.1111 0.0000
1478 52.3461 17.0000 0.0820 0.4426
... ... ... ... ...
53215 695.9634 9.0000 0.1143 0.0857
53411 108.2498 6.4211 0.1579 0.0000
53438 82.8410 20.5556 0.0707 0.0606
53440 84.5890 20.2549 0.0784 0.0588
53493 105.6803 5.8750 0.0000 0.0625
Pct_Dschrg_Home Pct_Dschrg_Hspc Pct_Dschrg_HomeHealth \
1259 0.6000 0.0000 0.1556
1359 0.3846 0.0000 0.1154
1404 0.6316 0.0000 0.0526
1432 0.4444 0.0000 0.2222
1478 0.3115 0.0000 0.0164
... ... ... ...
53215 0.5143 0.0286 0.0857
53411 0.2105 0.1579 0.3158
53438 0.5354 0.0101 0.1717
53440 0.5294 0.0098 0.1765
53493 0.5000 0.0625 0.1875
Pct_Dschrg_Other
1259 0.1556
1359 0.1923
1404 0.2105
1432 0.2222
1478 0.1475
... ...
53215 0.1714
53411 0.1579
53438 0.1515
53440 0.1471
53493 0.1875
[1357 rows x 19 columns]
Outliers in Total_Hosp_Per100K:
Year Month Bene_Geo_Desc Bene_Mdcd_Mdcr_Enrl_Stus \
1259 2020 1 National All
1359 2020 2 National All
1404 2020 2 National All
1432 2020 2 National All
1478 2020 3 National All
... ... ... ... ...
53215 2022 Second half North Dakota All
53411 2022 Second half Utah All
53438 2022 Second half Vermont All
53440 2022 Second half Vermont All
53493 2022 Second half West Virginia All
Bene_Race_Desc Bene_Sex_Desc Bene_Mdcr_Entlmt_Stus \
1259 Hispanic All Disabled
1359 Asian/Pacific Islander Male All
1404 Asian/Pacific Islander All All
1432 Black/African American All All
1478 American Indian/Alaska Native Male All
... ... ... ...
53215 All All ESRD
53411 Black/African American All All
53438 All All Disabled
53440 All All All
53493 Hispanic All All
Bene_Age_Desc Bene_RUCA_Desc Total_Hosp Total_Enrl \
1259 All All 45.0 958664.0
1359 All All 26.0 952855.0
1404 65-74 All 19.0 1147659.0
1432 All Rural 18.0 747724.0
1478 All All 61.0 116532.0
... ... ... ... ...
53215 All All 35.0 5029.0
53411 All All 19.0 17552.0
53438 All All 99.0 119506.0
53440 0-64 All 102.0 120583.0
53493 All All 16.0 15140.0
Total_Hosp_Per100K Avg_LOS Pct_Dschrg_SNF Pct_Dschrg_Expired \
1259 4.6940 15.2000 0.0222 0.0667
1359 2.7286 16.8846 0.0769 0.2308
1404 1.6555 28.9474 0.0526 0.0526
1432 2.4073 64.1667 0.1111 0.0000
1478 52.3461 17.0000 0.0820 0.4426
... ... ... ... ...
53215 695.9634 9.0000 0.1143 0.0857
53411 108.2498 6.4211 0.1579 0.0000
53438 82.8410 20.5556 0.0707 0.0606
53440 84.5890 20.2549 0.0784 0.0588
53493 105.6803 5.8750 0.0000 0.0625
Pct_Dschrg_Home Pct_Dschrg_Hspc Pct_Dschrg_HomeHealth \
1259 0.6000 0.0000 0.1556
1359 0.3846 0.0000 0.1154
1404 0.6316 0.0000 0.0526
1432 0.4444 0.0000 0.2222
1478 0.3115 0.0000 0.0164
... ... ... ...
53215 0.5143 0.0286 0.0857
53411 0.2105 0.1579 0.3158
53438 0.5354 0.0101 0.1717
53440 0.5294 0.0098 0.1765
53493 0.5000 0.0625 0.1875
Pct_Dschrg_Other
1259 0.1556
1359 0.1923
1404 0.2105
1432 0.2222
1478 0.1475
... ...
53215 0.1714
53411 0.1579
53438 0.1515
53440 0.1471
53493 0.1875
[1357 rows x 19 columns]
Outliers in Avg_LOS:
Year Month Bene_Geo_Desc Bene_Mdcd_Mdcr_Enrl_Stus \
1259 2020 1 National All
1359 2020 2 National All
1404 2020 2 National All
1432 2020 2 National All
1478 2020 3 National All
... ... ... ... ...
53215 2022 Second half North Dakota All
53411 2022 Second half Utah All
53438 2022 Second half Vermont All
53440 2022 Second half Vermont All
53493 2022 Second half West Virginia All
Bene_Race_Desc Bene_Sex_Desc Bene_Mdcr_Entlmt_Stus \
1259 Hispanic All Disabled
1359 Asian/Pacific Islander Male All
1404 Asian/Pacific Islander All All
1432 Black/African American All All
1478 American Indian/Alaska Native Male All
... ... ... ...
53215 All All ESRD
53411 Black/African American All All
53438 All All Disabled
53440 All All All
53493 Hispanic All All
Bene_Age_Desc Bene_RUCA_Desc Total_Hosp Total_Enrl \
1259 All All 45.0 958664.0
1359 All All 26.0 952855.0
1404 65-74 All 19.0 1147659.0
1432 All Rural 18.0 747724.0
1478 All All 61.0 116532.0
... ... ... ... ...
53215 All All 35.0 5029.0
53411 All All 19.0 17552.0
53438 All All 99.0 119506.0
53440 0-64 All 102.0 120583.0
53493 All All 16.0 15140.0
Total_Hosp_Per100K Avg_LOS Pct_Dschrg_SNF Pct_Dschrg_Expired \
1259 4.6940 15.2000 0.0222 0.0667
1359 2.7286 16.8846 0.0769 0.2308
1404 1.6555 28.9474 0.0526 0.0526
1432 2.4073 64.1667 0.1111 0.0000
1478 52.3461 17.0000 0.0820 0.4426
... ... ... ... ...
53215 695.9634 9.0000 0.1143 0.0857
53411 108.2498 6.4211 0.1579 0.0000
53438 82.8410 20.5556 0.0707 0.0606
53440 84.5890 20.2549 0.0784 0.0588
53493 105.6803 5.8750 0.0000 0.0625
Pct_Dschrg_Home Pct_Dschrg_Hspc Pct_Dschrg_HomeHealth \
1259 0.6000 0.0000 0.1556
1359 0.3846 0.0000 0.1154
1404 0.6316 0.0000 0.0526
1432 0.4444 0.0000 0.2222
1478 0.3115 0.0000 0.0164
... ... ... ...
53215 0.5143 0.0286 0.0857
53411 0.2105 0.1579 0.3158
53438 0.5354 0.0101 0.1717
53440 0.5294 0.0098 0.1765
53493 0.5000 0.0625 0.1875
Pct_Dschrg_Other
1259 0.1556
1359 0.1923
1404 0.2105
1432 0.2222
1478 0.1475
... ...
53215 0.1714
53411 0.1579
53438 0.1515
53440 0.1471
53493 0.1875
[1357 rows x 19 columns]
Outliers in Pct_Dschrg_SNF:
Year Month Bene_Geo_Desc Bene_Mdcd_Mdcr_Enrl_Stus \
1259 2020 1 National All
1359 2020 2 National All
1404 2020 2 National All
1432 2020 2 National All
1478 2020 3 National All
... ... ... ... ...
53215 2022 Second half North Dakota All
53411 2022 Second half Utah All
53438 2022 Second half Vermont All
53440 2022 Second half Vermont All
53493 2022 Second half West Virginia All
Bene_Race_Desc Bene_Sex_Desc Bene_Mdcr_Entlmt_Stus \
1259 Hispanic All Disabled
1359 Asian/Pacific Islander Male All
1404 Asian/Pacific Islander All All
1432 Black/African American All All
1478 American Indian/Alaska Native Male All
... ... ... ...
53215 All All ESRD
53411 Black/African American All All
53438 All All Disabled
53440 All All All
53493 Hispanic All All
Bene_Age_Desc Bene_RUCA_Desc Total_Hosp Total_Enrl \
1259 All All 45.0 958664.0
1359 All All 26.0 952855.0
1404 65-74 All 19.0 1147659.0
1432 All Rural 18.0 747724.0
1478 All All 61.0 116532.0
... ... ... ... ...
53215 All All 35.0 5029.0
53411 All All 19.0 17552.0
53438 All All 99.0 119506.0
53440 0-64 All 102.0 120583.0
53493 All All 16.0 15140.0
Total_Hosp_Per100K Avg_LOS Pct_Dschrg_SNF Pct_Dschrg_Expired \
1259 4.6940 15.2000 0.0222 0.0667
1359 2.7286 16.8846 0.0769 0.2308
1404 1.6555 28.9474 0.0526 0.0526
1432 2.4073 64.1667 0.1111 0.0000
1478 52.3461 17.0000 0.0820 0.4426
... ... ... ... ...
53215 695.9634 9.0000 0.1143 0.0857
53411 108.2498 6.4211 0.1579 0.0000
53438 82.8410 20.5556 0.0707 0.0606
53440 84.5890 20.2549 0.0784 0.0588
53493 105.6803 5.8750 0.0000 0.0625
Pct_Dschrg_Home Pct_Dschrg_Hspc Pct_Dschrg_HomeHealth \
1259 0.6000 0.0000 0.1556
1359 0.3846 0.0000 0.1154
1404 0.6316 0.0000 0.0526
1432 0.4444 0.0000 0.2222
1478 0.3115 0.0000 0.0164
... ... ... ...
53215 0.5143 0.0286 0.0857
53411 0.2105 0.1579 0.3158
53438 0.5354 0.0101 0.1717
53440 0.5294 0.0098 0.1765
53493 0.5000 0.0625 0.1875
Pct_Dschrg_Other
1259 0.1556
1359 0.1923
1404 0.2105
1432 0.2222
1478 0.1475
... ...
53215 0.1714
53411 0.1579
53438 0.1515
53440 0.1471
53493 0.1875
[1357 rows x 19 columns]
Outliers in Pct_Dschrg_Expired:
Year Month Bene_Geo_Desc Bene_Mdcd_Mdcr_Enrl_Stus \
1259 2020 1 National All
1359 2020 2 National All
1404 2020 2 National All
1432 2020 2 National All
1478 2020 3 National All
... ... ... ... ...
53215 2022 Second half North Dakota All
53411 2022 Second half Utah All
53438 2022 Second half Vermont All
53440 2022 Second half Vermont All
53493 2022 Second half West Virginia All
Bene_Race_Desc Bene_Sex_Desc Bene_Mdcr_Entlmt_Stus \
1259 Hispanic All Disabled
1359 Asian/Pacific Islander Male All
1404 Asian/Pacific Islander All All
1432 Black/African American All All
1478 American Indian/Alaska Native Male All
... ... ... ...
53215 All All ESRD
53411 Black/African American All All
53438 All All Disabled
53440 All All All
53493 Hispanic All All
Bene_Age_Desc Bene_RUCA_Desc Total_Hosp Total_Enrl \
1259 All All 45.0 958664.0
1359 All All 26.0 952855.0
1404 65-74 All 19.0 1147659.0
1432 All Rural 18.0 747724.0
1478 All All 61.0 116532.0
... ... ... ... ...
53215 All All 35.0 5029.0
53411 All All 19.0 17552.0
53438 All All 99.0 119506.0
53440 0-64 All 102.0 120583.0
53493 All All 16.0 15140.0
Total_Hosp_Per100K Avg_LOS Pct_Dschrg_SNF Pct_Dschrg_Expired \
1259 4.6940 15.2000 0.0222 0.0667
1359 2.7286 16.8846 0.0769 0.2308
1404 1.6555 28.9474 0.0526 0.0526
1432 2.4073 64.1667 0.1111 0.0000
1478 52.3461 17.0000 0.0820 0.4426
... ... ... ... ...
53215 695.9634 9.0000 0.1143 0.0857
53411 108.2498 6.4211 0.1579 0.0000
53438 82.8410 20.5556 0.0707 0.0606
53440 84.5890 20.2549 0.0784 0.0588
53493 105.6803 5.8750 0.0000 0.0625
Pct_Dschrg_Home Pct_Dschrg_Hspc Pct_Dschrg_HomeHealth \
1259 0.6000 0.0000 0.1556
1359 0.3846 0.0000 0.1154
1404 0.6316 0.0000 0.0526
1432 0.4444 0.0000 0.2222
1478 0.3115 0.0000 0.0164
... ... ... ...
53215 0.5143 0.0286 0.0857
53411 0.2105 0.1579 0.3158
53438 0.5354 0.0101 0.1717
53440 0.5294 0.0098 0.1765
53493 0.5000 0.0625 0.1875
Pct_Dschrg_Other
1259 0.1556
1359 0.1923
1404 0.2105
1432 0.2222
1478 0.1475
... ...
53215 0.1714
53411 0.1579
53438 0.1515
53440 0.1471
53493 0.1875
[1357 rows x 19 columns]
Outliers in Pct_Dschrg_Home:
Year Month Bene_Geo_Desc Bene_Mdcd_Mdcr_Enrl_Stus \
1259 2020 1 National All
1359 2020 2 National All
1404 2020 2 National All
1432 2020 2 National All
1478 2020 3 National All
... ... ... ... ...
53215 2022 Second half North Dakota All
53411 2022 Second half Utah All
53438 2022 Second half Vermont All
53440 2022 Second half Vermont All
53493 2022 Second half West Virginia All
Bene_Race_Desc Bene_Sex_Desc Bene_Mdcr_Entlmt_Stus \
1259 Hispanic All Disabled
1359 Asian/Pacific Islander Male All
1404 Asian/Pacific Islander All All
1432 Black/African American All All
1478 American Indian/Alaska Native Male All
... ... ... ...
53215 All All ESRD
53411 Black/African American All All
53438 All All Disabled
53440 All All All
53493 Hispanic All All
Bene_Age_Desc Bene_RUCA_Desc Total_Hosp Total_Enrl \
1259 All All 45.0 958664.0
1359 All All 26.0 952855.0
1404 65-74 All 19.0 1147659.0
1432 All Rural 18.0 747724.0
1478 All All 61.0 116532.0
... ... ... ... ...
53215 All All 35.0 5029.0
53411 All All 19.0 17552.0
53438 All All 99.0 119506.0
53440 0-64 All 102.0 120583.0
53493 All All 16.0 15140.0
Total_Hosp_Per100K Avg_LOS Pct_Dschrg_SNF Pct_Dschrg_Expired \
1259 4.6940 15.2000 0.0222 0.0667
1359 2.7286 16.8846 0.0769 0.2308
1404 1.6555 28.9474 0.0526 0.0526
1432 2.4073 64.1667 0.1111 0.0000
1478 52.3461 17.0000 0.0820 0.4426
... ... ... ... ...
53215 695.9634 9.0000 0.1143 0.0857
53411 108.2498 6.4211 0.1579 0.0000
53438 82.8410 20.5556 0.0707 0.0606
53440 84.5890 20.2549 0.0784 0.0588
53493 105.6803 5.8750 0.0000 0.0625
Pct_Dschrg_Home Pct_Dschrg_Hspc Pct_Dschrg_HomeHealth \
1259 0.6000 0.0000 0.1556
1359 0.3846 0.0000 0.1154
1404 0.6316 0.0000 0.0526
1432 0.4444 0.0000 0.2222
1478 0.3115 0.0000 0.0164
... ... ... ...
53215 0.5143 0.0286 0.0857
53411 0.2105 0.1579 0.3158
53438 0.5354 0.0101 0.1717
53440 0.5294 0.0098 0.1765
53493 0.5000 0.0625 0.1875
Pct_Dschrg_Other
1259 0.1556
1359 0.1923
1404 0.2105
1432 0.2222
1478 0.1475
... ...
53215 0.1714
53411 0.1579
53438 0.1515
53440 0.1471
53493 0.1875
[1357 rows x 19 columns]
Outliers in Pct_Dschrg_Hspc:
Year Month Bene_Geo_Desc Bene_Mdcd_Mdcr_Enrl_Stus \
1259 2020 1 National All
1359 2020 2 National All
1404 2020 2 National All
1432 2020 2 National All
1478 2020 3 National All
... ... ... ... ...
53215 2022 Second half North Dakota All
53411 2022 Second half Utah All
53438 2022 Second half Vermont All
53440 2022 Second half Vermont All
53493 2022 Second half West Virginia All
Bene_Race_Desc Bene_Sex_Desc Bene_Mdcr_Entlmt_Stus \
1259 Hispanic All Disabled
1359 Asian/Pacific Islander Male All
1404 Asian/Pacific Islander All All
1432 Black/African American All All
1478 American Indian/Alaska Native Male All
... ... ... ...
53215 All All ESRD
53411 Black/African American All All
53438 All All Disabled
53440 All All All
53493 Hispanic All All
Bene_Age_Desc Bene_RUCA_Desc Total_Hosp Total_Enrl \
1259 All All 45.0 958664.0
1359 All All 26.0 952855.0
1404 65-74 All 19.0 1147659.0
1432 All Rural 18.0 747724.0
1478 All All 61.0 116532.0
... ... ... ... ...
53215 All All 35.0 5029.0
53411 All All 19.0 17552.0
53438 All All 99.0 119506.0
53440 0-64 All 102.0 120583.0
53493 All All 16.0 15140.0
Total_Hosp_Per100K Avg_LOS Pct_Dschrg_SNF Pct_Dschrg_Expired \
1259 4.6940 15.2000 0.0222 0.0667
1359 2.7286 16.8846 0.0769 0.2308
1404 1.6555 28.9474 0.0526 0.0526
1432 2.4073 64.1667 0.1111 0.0000
1478 52.3461 17.0000 0.0820 0.4426
... ... ... ... ...
53215 695.9634 9.0000 0.1143 0.0857
53411 108.2498 6.4211 0.1579 0.0000
53438 82.8410 20.5556 0.0707 0.0606
53440 84.5890 20.2549 0.0784 0.0588
53493 105.6803 5.8750 0.0000 0.0625
Pct_Dschrg_Home Pct_Dschrg_Hspc Pct_Dschrg_HomeHealth \
1259 0.6000 0.0000 0.1556
1359 0.3846 0.0000 0.1154
1404 0.6316 0.0000 0.0526
1432 0.4444 0.0000 0.2222
1478 0.3115 0.0000 0.0164
... ... ... ...
53215 0.5143 0.0286 0.0857
53411 0.2105 0.1579 0.3158
53438 0.5354 0.0101 0.1717
53440 0.5294 0.0098 0.1765
53493 0.5000 0.0625 0.1875
Pct_Dschrg_Other
1259 0.1556
1359 0.1923
1404 0.2105
1432 0.2222
1478 0.1475
... ...
53215 0.1714
53411 0.1579
53438 0.1515
53440 0.1471
53493 0.1875
[1357 rows x 19 columns]
Outliers in Pct_Dschrg_HomeHealth:
Year Month Bene_Geo_Desc Bene_Mdcd_Mdcr_Enrl_Stus \
1259 2020 1 National All
1359 2020 2 National All
1404 2020 2 National All
1432 2020 2 National All
1478 2020 3 National All
... ... ... ... ...
53215 2022 Second half North Dakota All
53411 2022 Second half Utah All
53438 2022 Second half Vermont All
53440 2022 Second half Vermont All
53493 2022 Second half West Virginia All
Bene_Race_Desc Bene_Sex_Desc Bene_Mdcr_Entlmt_Stus \
1259 Hispanic All Disabled
1359 Asian/Pacific Islander Male All
1404 Asian/Pacific Islander All All
1432 Black/African American All All
1478 American Indian/Alaska Native Male All
... ... ... ...
53215 All All ESRD
53411 Black/African American All All
53438 All All Disabled
53440 All All All
53493 Hispanic All All
Bene_Age_Desc Bene_RUCA_Desc Total_Hosp Total_Enrl \
1259 All All 45.0 958664.0
1359 All All 26.0 952855.0
1404 65-74 All 19.0 1147659.0
1432 All Rural 18.0 747724.0
1478 All All 61.0 116532.0
... ... ... ... ...
53215 All All 35.0 5029.0
53411 All All 19.0 17552.0
53438 All All 99.0 119506.0
53440 0-64 All 102.0 120583.0
53493 All All 16.0 15140.0
Total_Hosp_Per100K Avg_LOS Pct_Dschrg_SNF Pct_Dschrg_Expired \
1259 4.6940 15.2000 0.0222 0.0667
1359 2.7286 16.8846 0.0769 0.2308
1404 1.6555 28.9474 0.0526 0.0526
1432 2.4073 64.1667 0.1111 0.0000
1478 52.3461 17.0000 0.0820 0.4426
... ... ... ... ...
53215 695.9634 9.0000 0.1143 0.0857
53411 108.2498 6.4211 0.1579 0.0000
53438 82.8410 20.5556 0.0707 0.0606
53440 84.5890 20.2549 0.0784 0.0588
53493 105.6803 5.8750 0.0000 0.0625
Pct_Dschrg_Home Pct_Dschrg_Hspc Pct_Dschrg_HomeHealth \
1259 0.6000 0.0000 0.1556
1359 0.3846 0.0000 0.1154
1404 0.6316 0.0000 0.0526
1432 0.4444 0.0000 0.2222
1478 0.3115 0.0000 0.0164
... ... ... ...
53215 0.5143 0.0286 0.0857
53411 0.2105 0.1579 0.3158
53438 0.5354 0.0101 0.1717
53440 0.5294 0.0098 0.1765
53493 0.5000 0.0625 0.1875
Pct_Dschrg_Other
1259 0.1556
1359 0.1923
1404 0.2105
1432 0.2222
1478 0.1475
... ...
53215 0.1714
53411 0.1579
53438 0.1515
53440 0.1471
53493 0.1875
[1357 rows x 19 columns]
Outliers in Pct_Dschrg_Other:
Year Month Bene_Geo_Desc Bene_Mdcd_Mdcr_Enrl_Stus \
1259 2020 1 National All
1359 2020 2 National All
1404 2020 2 National All
1432 2020 2 National All
1478 2020 3 National All
... ... ... ... ...
53215 2022 Second half North Dakota All
53411 2022 Second half Utah All
53438 2022 Second half Vermont All
53440 2022 Second half Vermont All
53493 2022 Second half West Virginia All
Bene_Race_Desc Bene_Sex_Desc Bene_Mdcr_Entlmt_Stus \
1259 Hispanic All Disabled
1359 Asian/Pacific Islander Male All
1404 Asian/Pacific Islander All All
1432 Black/African American All All
1478 American Indian/Alaska Native Male All
... ... ... ...
53215 All All ESRD
53411 Black/African American All All
53438 All All Disabled
53440 All All All
53493 Hispanic All All
Bene_Age_Desc Bene_RUCA_Desc Total_Hosp Total_Enrl \
1259 All All 45.0 958664.0
1359 All All 26.0 952855.0
1404 65-74 All 19.0 1147659.0
1432 All Rural 18.0 747724.0
1478 All All 61.0 116532.0
... ... ... ... ...
53215 All All 35.0 5029.0
53411 All All 19.0 17552.0
53438 All All 99.0 119506.0
53440 0-64 All 102.0 120583.0
53493 All All 16.0 15140.0
Total_Hosp_Per100K Avg_LOS Pct_Dschrg_SNF Pct_Dschrg_Expired \
1259 4.6940 15.2000 0.0222 0.0667
1359 2.7286 16.8846 0.0769 0.2308
1404 1.6555 28.9474 0.0526 0.0526
1432 2.4073 64.1667 0.1111 0.0000
1478 52.3461 17.0000 0.0820 0.4426
... ... ... ... ...
53215 695.9634 9.0000 0.1143 0.0857
53411 108.2498 6.4211 0.1579 0.0000
53438 82.8410 20.5556 0.0707 0.0606
53440 84.5890 20.2549 0.0784 0.0588
53493 105.6803 5.8750 0.0000 0.0625
Pct_Dschrg_Home Pct_Dschrg_Hspc Pct_Dschrg_HomeHealth \
1259 0.6000 0.0000 0.1556
1359 0.3846 0.0000 0.1154
1404 0.6316 0.0000 0.0526
1432 0.4444 0.0000 0.2222
1478 0.3115 0.0000 0.0164
... ... ... ...
53215 0.5143 0.0286 0.0857
53411 0.2105 0.1579 0.3158
53438 0.5354 0.0101 0.1717
53440 0.5294 0.0098 0.1765
53493 0.5000 0.0625 0.1875
Pct_Dschrg_Other
1259 0.1556
1359 0.1923
1404 0.2105
1432 0.2222
1478 0.1475
... ...
53215 0.1714
53411 0.1579
53438 0.1515
53440 0.1471
53493 0.1875
[1357 rows x 19 columns]
Quality Score for Outliers
# Specify the columns to analyze for outliers
columns_to_check = ['Total_Hosp', 'Total_Enrl', 'Total_Hosp_Per100K', 'Avg_LOS',
'Pct_Dschrg_SNF', 'Pct_Dschrg_Expired', 'Pct_Dschrg_Home',
'Pct_Dschrg_Hspc', 'Pct_Dschrg_HomeHealth', 'Pct_Dschrg_Other']
# Create a figure to display the data and outliers
plt.figure(figsize=(16, 10))
outlier_counts = {} # Store outlier counts for each column
total_data_points = len(df1) # Total data points in the dataset
# Loop through the specified columns
for i, column_name in enumerate(columns_to_check, 1):
plt.subplot(2, 5, i)
# Create a boxplot to visualize the distribution
sns.boxplot(data=df1, y=column_name)
plt.title(f'Boxplot for {column_name}')
# Calculate the Interquartile Range (IQR)
Q1 = df1[column_name].quantile(0.25)
Q3 = df1[column_name].quantile(0.75)
IQR = Q3 - Q1
# Define the lower and upper bounds to identify outliers
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
# Identify outliers
outliers = df1[(df1[column_name] < lower_bound) | (df1[column_name] > upper_bound)]
outlier_counts[column_name] = len(outliers)
# Plot the outliers as red points on the boxplot
sns.scatterplot(data=outliers, x=outliers.index, y=column_name, color='red', label='Outliers')
# Print the identified outliers for each column
for column_name in columns_to_check:
print(f'Outliers in {column_name}: {outlier_counts[column_name]}')
# Calculate the quality score for outlier detection
max_score = 100 # Maximum score
# Calculate the percentage of outliers for each column
outlier_percentages = [(outlier_counts[column] / total_data_points) * 100 for column in columns_to_check]
# Calculate the overall quality score as the average of outlier percentages
quality_score_outliers = max_score - sum(outlier_percentages) / len(columns_to_check)
print(f'Quality Score for Outliers: {quality_score_outliers:.2f}')
Outliers in Total_Hosp: 6315 Outliers in Total_Enrl: 6882 Outliers in Total_Hosp_Per100K: 4339 Outliers in Avg_LOS: 1957 Outliers in Pct_Dschrg_SNF: 723 Outliers in Pct_Dschrg_Expired: 474 Outliers in Pct_Dschrg_Home: 878 Outliers in Pct_Dschrg_Hspc: 1588 Outliers in Pct_Dschrg_HomeHealth: 818 Outliers in Pct_Dschrg_Other: 1357 Quality Score for Outliers: 95.33
Outliers for Faclevel Dataset
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Specify the columns to analyze for outliers
columns_to_check = ['Residents Weekly Admissions COVID-19', 'Residents Total Admissions COVID-19',
'Residents Weekly Confirmed COVID-19', 'Residents Total Confirmed COVID-19',
'Residents Weekly Suspected COVID-19', 'Residents Total Suspected COVID-19',
'Residents Weekly All Deaths', 'Residents Total All Deaths',
'Residents Weekly COVID-19 Deaths', 'Residents Total COVID-19 Deaths',
'Number of All Beds', 'Total Number of Occupied Beds',
'Staff Weekly Confirmed COVID-19', 'Staff Total Confirmed COVID-19',
'Staff Weekly Suspected COVID-19', 'Staff Total Suspected COVID-19',
'Staff Weekly COVID-19 Deaths', 'Staff Total COVID-19 Deaths',
'Total Resident Confirmed COVID-19 Cases Per 1,000 Residents',
'Total Resident COVID-19 Deaths Per 1,000 Residents',
'Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases']
# Calculate the number of rows and columns for the subplot layout
num_columns = 4 # Adjust this number as needed
num_rows = (len(columns_to_check) + num_columns - 1) // num_columns
# Create a figure to display the data and outliers
plt.figure(figsize=(32, 20))
# Loop through the specified columns
for i, column_name in enumerate(columns_to_check, 1):
plt.subplot(num_rows, num_columns, i)
# Create a boxplot to visualize the distribution
sns.boxplot(data=df2, y=column_name)
plt.title(f'Boxplot for {column_name}')
# Calculate the Interquartile Range (IQR)
Q1 = df2[column_name].quantile(0.25)
Q3 = df2[column_name].quantile(0.75)
IQR = Q3 - Q1
# Define the lower and upper bounds to identify outliers
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
# Identify outliers
outliers = df2[(df2[column_name] < lower_bound) | (df2[column_name] > upper_bound)]
# Plot the outliers as red points on the boxplot
sns.scatterplot(data=outliers, x=outliers.index, y=column_name, color='red', label='Outliers')
# Adjust layout
plt.tight_layout()
# Show the plot
plt.show()
# Print the identified outliers for each column
for column_name in columns_to_check:
print(f'Outliers in {column_name}:')
print(outliers)
Outliers in Residents Weekly Admissions COVID-19:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Residents Total Admissions COVID-19:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Residents Weekly Confirmed COVID-19:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Residents Total Confirmed COVID-19:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Residents Weekly Suspected COVID-19:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Residents Total Suspected COVID-19:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Residents Weekly All Deaths:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Residents Total All Deaths:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Residents Weekly COVID-19 Deaths:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Residents Total COVID-19 Deaths:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Number of All Beds:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Total Number of Occupied Beds:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Staff Weekly Confirmed COVID-19:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Staff Total Confirmed COVID-19:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Staff Weekly Suspected COVID-19:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Staff Total Suspected COVID-19:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Staff Weekly COVID-19 Deaths:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Staff Total COVID-19 Deaths:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Total Resident Confirmed COVID-19 Cases Per 1,000 Residents:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Total Resident COVID-19 Deaths Per 1,000 Residents:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Outliers in Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases:
Week Ending Federal Provider Number \
49 05/31/20 015040
107 06/07/20 015084
120 05/24/20 015100
121 05/31/20 015100
122 06/07/20 015100
... ... ...
45222 06/07/20 676104
45297 06/07/20 676137
45583 05/24/20 676246
45865 05/24/20 676350
45866 05/31/20 676350
Provider Name \
49 DIVERSICARE OF MONTGOMERY
107 PARK PLACE
120 CROWNE HEALTH CARE OF MOBILE
121 CROWNE HEALTH CARE OF MOBILE
122 CROWNE HEALTH CARE OF MOBILE
... ...
45222 KINDRED TRANSITIONAL CARE AND REHABILITATION-G...
45297 LEGEND OAKS HEALTHCARE AND REHABILITATION CENT...
45583 RIVERSIDE NURSING AND REHABILITATION CENTER
45865 THE HEIGHTS OF TOMBALL
45866 THE HEIGHTS OF TOMBALL
Provider Address Provider City Provider State \
49 2020 NORTH COUNTRY CLUB DRIVE MONTGOMERY AL
107 100 PARK PLACE SELMA AL
120 954 NAVCO ROAD MOBILE AL
121 954 NAVCO ROAD MOBILE AL
122 954 NAVCO ROAD MOBILE AL
... ... ... ...
45222 1005 IRA E. WOODS PARKWAY GRAPEVINE TX
45297 8902 WEST RD HOUSTON TX
45583 6801 E RIVERSIDE DR AUSTIN TX
45865 27840 JOHNSON ROAD TOMBALL TX
45866 27840 JOHNSON ROAD TOMBALL TX
Provider Zip Code Submitted Data Passed Quality Assurance Check \
49 36106.0 Y Y
107 36701.0 Y Y
120 36605.0 Y Y
121 36605.0 Y Y
122 36605.0 Y Y
... ... ... ...
45222 76051.0 Y Y
45297 77064.0 Y Y
45583 78741.0 Y Y
45865 77375.0 Y Y
45866 77375.0 Y Y
Residents Weekly Admissions COVID-19 ... \
49 0.0 ...
107 0.0 ...
120 13.0 ...
121 0.0 ...
122 2.0 ...
... ... ...
45222 0.0 ...
45297 0.0 ...
45583 0.0 ...
45865 0.0 ...
45866 0.0 ...
Any Current Supply of Hand Sanitizer \
49 Y
107 Y
120 Y
121 Y
122 Y
... ...
45222 Y
45297 Y
45583 Y
45865 Y
45866 Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
49 Y N
107 Y N
120 Y N
121 Y N
122 Y N
... ... ...
45222 Y N
45297 Y N
45583 Y N
45865 Y N
45866 Y N
Number of Ventilators in Facility \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Number of Ventilators in Use for COVID-19 \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Any Current Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
One-Week Supply of Ventilator Supplies \
49 NaN
107 NaN
120 NaN
121 NaN
122 NaN
... ...
45222 NaN
45297 NaN
45583 NaN
45865 NaN
45866 NaN
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
49 20.4
107 11.4
120 10.1
121 20.4
122 30.6
... ...
45222 52.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
49 20.4
107 11.4
120 50.5
121 51.0
122 51.0
... ...
45222 131.6
45297 13.0
45583 14.7
45865 11.0
45866 11.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
49 100.0
107 100.0
120 500.0
121 250.0
122 166.7
... ...
45222 250.0
45297 100.0
45583 100.0
45865 100.0
45866 100.0
[1301 rows x 55 columns]
Quality Score for Outliers
# Specify the columns to analyze for outliers
columns_to_check = ['Residents Weekly Admissions COVID-19', 'Residents Total Admissions COVID-19',
'Residents Weekly Confirmed COVID-19', 'Residents Total Confirmed COVID-19',
'Residents Weekly Suspected COVID-19', 'Residents Total Suspected COVID-19',
'Residents Weekly All Deaths', 'Residents Total All Deaths',
'Residents Weekly COVID-19 Deaths', 'Residents Total COVID-19 Deaths',
'Number of All Beds', 'Total Number of Occupied Beds',
'Staff Weekly Confirmed COVID-19', 'Staff Total Confirmed COVID-19',
'Staff Weekly Suspected COVID-19', 'Staff Total Suspected COVID-19',
'Staff Weekly COVID-19 Deaths', 'Staff Total COVID-19 Deaths',
'Total Resident Confirmed COVID-19 Cases Per 1,000 Residents',
'Total Resident COVID-19 Deaths Per 1,000 Residents',
'Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases']
# Create a figure to display the data and outliers
plt.figure(figsize=(32, 20))
outlier_counts = {} # Store outlier counts for each column
total_data_points = len(df2) # Total data points in the dataset
# Loop through the specified columns
for i, column_name in enumerate(columns_to_check, 1):
plt.subplot(num_rows, num_columns, i)
# Create a boxplot to visualize the distribution
sns.boxplot(data=df2, y=column_name)
plt.title(f'Boxplot for {column_name}')
# Calculate the Interquartile Range (IQR)
Q1 = df2[column_name].quantile(0.25)
Q3 = df2[column_name].quantile(0.75)
IQR = Q3 - Q1
# Define the lower and upper bounds to identify outliers
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
# Identify outliers
outliers = df2[(df2[column_name] < lower_bound) | (df2[column_name] > upper_bound)]
outlier_counts[column_name] = len(outliers)
# Plot the outliers as red points on the boxplot
sns.scatterplot(data=outliers, x=outliers.index, y=column_name, color='red', label='Outliers')
# Print the identified outliers for each column
for column_name in columns_to_check:
print(f'Outliers in {column_name}: {outlier_counts[column_name]}')
# Calculate the quality score for outlier detection
max_score = 100 # Maximum score
# Calculate the percentage of outliers for each column
outlier_percentages = [(outlier_counts[column] / total_data_points) * 100 for column in columns_to_check]
# Calculate the overall quality score as the average of outlier percentages
quality_score_outliers = max_score - sum(outlier_percentages) / len(columns_to_check)
print(f'Quality Score for Outliers: {quality_score_outliers:.2f}')
Outliers in Residents Weekly Admissions COVID-19: 7517 Outliers in Residents Total Admissions COVID-19: 7267 Outliers in Residents Weekly Confirmed COVID-19: 7638 Outliers in Residents Total Confirmed COVID-19: 9310 Outliers in Residents Weekly Suspected COVID-19: 8465 Outliers in Residents Total Suspected COVID-19: 6319 Outliers in Residents Weekly All Deaths: 6276 Outliers in Residents Total All Deaths: 4141 Outliers in Residents Weekly COVID-19 Deaths: 5327 Outliers in Residents Total COVID-19 Deaths: 9168 Outliers in Number of All Beds: 1641 Outliers in Total Number of Occupied Beds: 1485 Outliers in Staff Weekly Confirmed COVID-19: 9805 Outliers in Staff Total Confirmed COVID-19: 7247 Outliers in Staff Weekly Suspected COVID-19: 9257 Outliers in Staff Total Suspected COVID-19: 6053 Outliers in Staff Weekly COVID-19 Deaths: 355 Outliers in Staff Total COVID-19 Deaths: 835 Outliers in Total Resident Confirmed COVID-19 Cases Per 1,000 Residents: 8593 Outliers in Total Resident COVID-19 Deaths Per 1,000 Residents: 9153 Outliers in Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases: 1301 Quality Score for Outliers: 86.91
Statistics Summary for Covid 19 Dataset
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Specify the numerical columns for which you want to calculate summary statistics
numerical_columns = ['Total_Hosp', 'Total_Enrl', 'Total_Hosp_Per100K', 'Avg_LOS',
'Pct_Dschrg_SNF', 'Pct_Dschrg_Expired', 'Pct_Dschrg_Home',
'Pct_Dschrg_Hspc', 'Pct_Dschrg_HomeHealth', 'Pct_Dschrg_Other']
# Create a scatter plot for the selected numerical columns
plt.figure(figsize=(12, 8))
sns.scatterplot(data=df1[numerical_columns], alpha=0.5)
plt.title("Scatter Plot of Numerical Columns")
plt.xlabel("X-Axis (Value)")
plt.ylabel("Y-Axis (Value)")
# Show the plot
plt.tight_layout()
plt.show()
# Calculate and print summary statistics for the numerical columns
summary_stats = df1[numerical_columns].describe()
print("Summary Statistics:")
print(summary_stats)
Summary Statistics:
Total_Hosp Total_Enrl Total_Hosp_Per100K Avg_LOS \
count 4.356400e+04 5.409100e+04 43529.000000 40454.000000
mean 4.871266e+03 1.972652e+06 315.248420 10.672833
std 3.038648e+04 1.160444e+07 4664.812388 3.895076
min 0.000000e+00 0.000000e+00 0.000000 3.500000
25% 7.300000e+01 2.427650e+04 56.500600 8.866125
50% 3.440000e+02 1.847740e+05 125.274000 10.084850
75% 1.502000e+03 7.760705e+05 240.982800 11.545500
max 1.150216e+06 3.418546e+08 619926.199300 164.518500
Pct_Dschrg_SNF Pct_Dschrg_Expired Pct_Dschrg_Home Pct_Dschrg_Hspc \
count 40454.000000 40454.000000 40454.000000 40454.000000
mean 0.169851 0.128320 0.421465 0.037739
std 0.072631 0.068620 0.112389 0.027012
min 0.000000 0.000000 0.000000 0.000000
25% 0.121200 0.072000 0.354800 0.020100
50% 0.164200 0.125900 0.416700 0.035400
75% 0.211800 0.170975 0.490400 0.049400
max 0.769200 0.666700 1.000000 0.307700
Pct_Dschrg_HomeHealth Pct_Dschrg_Other
count 40454.000000 40454.000000
mean 0.170100 0.072524
std 0.061144 0.035411
min 0.000000 0.000000
25% 0.131300 0.051900
50% 0.168600 0.068500
75% 0.207200 0.089000
max 0.583300 0.461500
Quality score for Statistical Summary
# Specify the numerical columns for which you want to calculate summary statistics
numerical_columns = ['Total_Hosp', 'Total_Enrl', 'Total_Hosp_Per100K', 'Avg_LOS',
'Pct_Dschrg_SNF', 'Pct_Dschrg_Expired', 'Pct_Dschrg_Home',
'Pct_Dschrg_Hspc', 'Pct_Dschrg_HomeHealth', 'Pct_Dschrg_Other']
# Calculate summary statistics for the numerical columns
summary_stats = df1[numerical_columns].describe()
# Calculate the quality score for statistical summary
max_score = 100 # Maximum score
# Calculate the range of each numerical column
column_ranges = summary_stats.loc['max'] - summary_stats.loc['min']
# Calculate the overall quality score as the average of the ranges
quality_score_stats = max_score - (column_ranges.mean() / column_ranges.max()) * max_score
print(f'Quality Score for Statistical Summary: {quality_score_stats:.2f}')
Quality Score for Statistical Summary: 89.95
Statistics Summary for Faclevel Dataset
import pandas as pd
import matplotlib.pyplot as plt
# Specify the numerical columns for which you want to calculate summary statistics
numerical_columns = ['Residents Weekly Admissions COVID-19', 'Residents Total Admissions COVID-19',
'Residents Weekly Confirmed COVID-19', 'Residents Total Confirmed COVID-19',
'Residents Weekly Suspected COVID-19', 'Residents Total Suspected COVID-19',
'Residents Weekly All Deaths', 'Residents Total All Deaths',
'Residents Weekly COVID-19 Deaths', 'Residents Total COVID-19 Deaths',
'Number of All Beds', 'Total Number of Occupied Beds',
'Staff Weekly Confirmed COVID-19', 'Staff Total Confirmed COVID-19',
'Staff Weekly Suspected COVID-19', 'Staff Total Suspected COVID-19',
'Staff Weekly COVID-19 Deaths', 'Staff Total COVID-19 Deaths',
'Total Resident Confirmed COVID-19 Cases Per 1,000 Residents',
'Total Resident COVID-19 Deaths Per 1,000 Residents',
'Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases']
# Calculate summary statistics
summary_stats = df2[numerical_columns].describe()
# Calculate custom percentiles
percentiles = [10, 25, 50, 75, 90]
custom_percentiles = df2[numerical_columns].quantile([p / 100 for p in percentiles])
# Rename the index of the custom percentiles
custom_percentiles.index = [f'{p}%' for p in percentiles]
# Print summary statistics
print("Summary Statistics:")
print(summary_stats)
# Print custom percentiles
print("\nCustom Percentiles:")
print(custom_percentiles)
# Combine summary statistics and custom percentiles
combined_stats = pd.concat([summary_stats, custom_percentiles])
# Create a box plot for the numerical columns
plt.figure(figsize=(12, 8))
df2[numerical_columns].boxplot(vert=False, sym='r')
plt.title("Box Plot of Numerical Columns")
plt.xlabel("Value")
plt.xticks(rotation=45)
# Show the plot
plt.tight_layout()
plt.show()
Summary Statistics:
Residents Weekly Admissions COVID-19 \
count 42825.000000
mean 0.998342
std 5.591550
min 0.000000
25% 0.000000
50% 0.000000
75% 0.000000
max 224.000000
Residents Total Admissions COVID-19 \
count 42825.000000
mean 2.530438
std 9.671523
min 0.000000
25% 0.000000
50% 0.000000
75% 1.000000
max 241.000000
Residents Weekly Confirmed COVID-19 \
count 42825.000000
mean 2.507624
std 10.878217
min 0.000000
25% 0.000000
50% 0.000000
75% 0.000000
max 250.000000
Residents Total Confirmed COVID-19 \
count 42825.000000
mean 6.741670
std 18.870014
min 0.000000
25% 0.000000
50% 0.000000
75% 1.000000
max 409.000000
Residents Weekly Suspected COVID-19 \
count 42825.000000
mean 1.664402
std 9.242252
min 0.000000
25% 0.000000
50% 0.000000
75% 0.000000
max 291.000000
Residents Total Suspected COVID-19 Residents Weekly All Deaths \
count 42825.000000 42825.000000
mean 4.329154 1.997128
std 16.163922 7.441761
min 0.000000 0.000000
25% 0.000000 0.000000
50% 0.000000 0.000000
75% 2.000000 1.000000
max 473.000000 522.000000
Residents Total All Deaths Residents Weekly COVID-19 Deaths \
count 42825.000000 42825.000000
mean 5.285954 0.688780
std 12.719423 3.439776
min 0.000000 0.000000
25% 0.000000 0.000000
50% 1.000000 0.000000
75% 6.000000 0.000000
max 522.000000 117.000000
Residents Total COVID-19 Deaths ... Total Number of Occupied Beds \
count 42825.000000 ... 42734.000000
mean 1.825616 ... 77.169350
std 5.990281 ... 47.167811
min 0.000000 ... 0.000000
25% 0.000000 ... 47.000000
50% 0.000000 ... 70.000000
75% 0.000000 ... 96.000000
max 127.000000 ... 1274.000000
Staff Weekly Confirmed COVID-19 Staff Total Confirmed COVID-19 \
count 42825.000000 42825.000000
mean 1.549726 4.000490
std 6.153438 10.871383
min 0.000000 0.000000
25% 0.000000 0.000000
50% 0.000000 0.000000
75% 0.000000 2.000000
max 254.000000 281.000000
Staff Weekly Suspected COVID-19 Staff Total Suspected COVID-19 \
count 42825.000000 42825.000000
mean 1.511617 3.943117
std 20.714332 36.303646
min 0.000000 0.000000
25% 0.000000 0.000000
50% 0.000000 0.000000
75% 0.000000 2.000000
max 3728.000000 3728.000000
Staff Weekly COVID-19 Deaths Staff Total COVID-19 Deaths \
count 42825.000000 42825.000000
mean 0.012469 0.031103
std 0.183459 0.303267
min 0.000000 0.000000
25% 0.000000 0.000000
50% 0.000000 0.000000
75% 0.000000 0.000000
max 9.000000 9.000000
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
count 42625.000000
mean 83.444882
std 238.920757
min 0.000000
25% 0.000000
50% 0.000000
75% 18.900000
max 12000.000000
Total Resident COVID-19 Deaths Per 1,000 Residents \
count 42625.000000
mean 23.085896
std 77.643841
min 0.000000
25% 0.000000
50% 0.000000
75% 0.000000
max 3000.000000
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
count 12850.000000
mean 38.989689
std 108.996474
min 0.000000
25% 0.000000
50% 15.400000
75% 36.000000
max 2400.000000
[8 rows x 21 columns]
Custom Percentiles:
Residents Weekly Admissions COVID-19 \
10% 0.0
25% 0.0
50% 0.0
75% 0.0
90% 2.0
Residents Total Admissions COVID-19 Residents Weekly Confirmed COVID-19 \
10% 0.0 0.0
25% 0.0 0.0
50% 0.0 0.0
75% 1.0 0.0
90% 6.0 3.0
Residents Total Confirmed COVID-19 Residents Weekly Suspected COVID-19 \
10% 0.0 0.0
25% 0.0 0.0
50% 0.0 0.0
75% 1.0 0.0
90% 24.0 2.0
Residents Total Suspected COVID-19 Residents Weekly All Deaths \
10% 0.0 0.0
25% 0.0 0.0
50% 0.0 0.0
75% 2.0 1.0
90% 10.0 4.0
Residents Total All Deaths Residents Weekly COVID-19 Deaths \
10% 0.0 0.0
25% 0.0 0.0
50% 1.0 0.0
75% 6.0 0.0
90% 15.0 1.0
Residents Total COVID-19 Deaths ... Total Number of Occupied Beds \
10% 0.0 ... 32.0
25% 0.0 ... 47.0
50% 0.0 ... 70.0
75% 0.0 ... 96.0
90% 5.0 ... 127.0
Staff Weekly Confirmed COVID-19 Staff Total Confirmed COVID-19 \
10% 0.0 0.0
25% 0.0 0.0
50% 0.0 0.0
75% 0.0 2.0
90% 3.0 13.0
Staff Weekly Suspected COVID-19 Staff Total Suspected COVID-19 \
10% 0.0 0.0
25% 0.0 0.0
50% 0.0 0.0
75% 0.0 2.0
90% 2.0 8.0
Staff Weekly COVID-19 Deaths Staff Total COVID-19 Deaths \
10% 0.0 0.0
25% 0.0 0.0
50% 0.0 0.0
75% 0.0 0.0
90% 0.0 0.0
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
10% 0.00
25% 0.00
50% 0.00
75% 18.90
90% 292.54
Total Resident COVID-19 Deaths Per 1,000 Residents \
10% 0.0
25% 0.0
50% 0.0
75% 0.0
90% 69.2
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
10% 0.0
25% 0.0
50% 15.4
75% 36.0
90% 93.3
[5 rows x 21 columns]
Quality score for Statistical Summary
import pandas as pd
# Specify the numerical columns for which you want to calculate summary statistics
numerical_columns = ['Residents Weekly Admissions COVID-19', 'Residents Total Admissions COVID-19',
'Residents Weekly Confirmed COVID-19', 'Residents Total Confirmed COVID-19',
'Residents Weekly Suspected COVID-19', 'Residents Total Suspected COVID-19',
'Residents Weekly All Deaths', 'Residents Total All Deaths',
'Residents Weekly COVID-19 Deaths', 'Residents Total COVID-19 Deaths',
'Number of All Beds', 'Total Number of Occupied Beds',
'Staff Weekly Confirmed COVID-19', 'Staff Total Confirmed COVID-19',
'Staff Weekly Suspected COVID-19', 'Staff Total Suspected COVID-19',
'Staff Weekly COVID-19 Deaths', 'Staff Total COVID-19 Deaths',
'Total Resident Confirmed COVID-19 Cases Per 1,000 Residents',
'Total Resident COVID-19 Deaths Per 1,000 Residents',
'Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases']
# Calculate summary statistics for the numerical columns
summary_stats = df2[numerical_columns].describe()
# Calculate the quality score for statistical summary
max_score = 100 # Maximum score
# Calculate the range of each numerical column
column_ranges = summary_stats.loc['max'] - summary_stats.loc['min']
# Calculate the overall quality score as the average of the ranges
quality_score_stats = max_score - (column_ranges.mean() / column_ranges.max()) * max_score
print(f'Quality Score for Statistical Summary: {quality_score_stats:.2f}')
Quality Score for Statistical Summary: 87.83
Categorical Cardinality for Covid 19 Dataset
import pandas as pd
import matplotlib.pyplot as plt
# Specify the categorical columns for which you want to count unique values
categorical_columns = ['Year', 'Month', 'Bene_Geo_Desc', 'Bene_Mdcd_Mdcr_Enrl_Stus', 'Bene_Race_Desc',
'Bene_Sex_Desc', 'Bene_Mdcr_Entlmt_Stus', 'Bene_Age_Desc', 'Bene_RUCA_Desc']
# Count unique values in each categorical column
unique_value_counts = {}
for column in categorical_columns:
unique_values = df1[column].nunique()
unique_value_counts[column] = unique_values
# Print the counts of unique values for each categorical column
print("Categorical Cardinality (Unique Value Counts):")
for column, count in unique_value_counts.items():
print(f"{column}: {count}")
# Plot the counts of unique values as a bar plot
plt.figure(figsize=(12, 6))
plt.barh(list(unique_value_counts.keys()), unique_value_counts.values(), color='skyblue')
plt.title("Categorical Cardinality (Unique Value Counts)")
plt.xlabel("Count of Unique Values")
plt.ylabel("Categorical Columns")
# Show the plot
plt.tight_layout()
plt.show()
Categorical Cardinality (Unique Value Counts): Year: 3 Month: 15 Bene_Geo_Desc: 55 Bene_Mdcd_Mdcr_Enrl_Stus: 3 Bene_Race_Desc: 7 Bene_Sex_Desc: 3 Bene_Mdcr_Entlmt_Stus: 4 Bene_Age_Desc: 5 Bene_RUCA_Desc: 4
Quality Score for Categorical Cardinality
import pandas as pd
import matplotlib.pyplot as plt
# Specify the categorical columns for which you want to count unique values
categorical_columns = ['Year', 'Month', 'Bene_Geo_Desc', 'Bene_Mdcd_Mdcr_Enrl_Stus', 'Bene_Race_Desc',
'Bene_Sex_Desc', 'Bene_Mdcr_Entlmt_Stus', 'Bene_Age_Desc', 'Bene_RUCA_Desc']
# Count unique values in each categorical column
unique_value_counts = {}
for column in categorical_columns:
unique_values = df1[column].nunique()
unique_value_counts[column] = unique_values
# Calculate the quality score for categorical columns
max_score = 100 # Maximum score
# Calculate the overall quality score as the average of the unique value counts
quality_score_categorical = max_score - (sum(unique_value_counts.values()) / (len(categorical_columns) * max_score))
print(f'Quality Score for Categorical Columns: {quality_score_categorical:.2f}')
Quality Score for Categorical Columns: 99.89
Categorical Cardinality for Faclevel Dataset
import pandas as pd
import matplotlib.pyplot as plt
# Specify the categorical columns for which you want to count unique values
categorical_columns = ['Week Ending', 'Federal Provider Number', 'Provider Name', 'Provider Address',
'Provider City', 'Provider State', 'Provider Zip Code', 'Submitted Data',
'Passed Quality Assurance Check', 'Residents Weekly Admissions COVID-19',
'Residents Total Admissions COVID-19', 'Residents Weekly Confirmed COVID-19',
'Residents Total Confirmed COVID-19', 'Residents Weekly Suspected COVID-19',
'Residents Total Suspected COVID-19', 'Residents Weekly All Deaths',
'Residents Total All Deaths', 'Residents Weekly COVID-19 Deaths',
'Residents Total COVID-19 Deaths', 'Number of All Beds', 'Total Number of Occupied Beds',
'Resident Access to Testing in Facility', 'Laboratory Type Is State Health Dept',
'Laboratory Type Is Private Lab', 'Laboratory Type Is Other', 'Staff Weekly Confirmed COVID-19',
'Staff Total Confirmed COVID-19', 'Staff Weekly Suspected COVID-19', 'Staff Total Suspected COVID-19',
'Staff Weekly COVID-19 Deaths', 'Staff Total COVID-19 Deaths', 'Shortage of Nursing Staff',
'Shortage of Clinical Staff', 'Shortage of Aides', 'Shortage of Other Staff',
'Any Current Supply of N95 Masks', 'One-Week Supply of N95 Masks',
'Any Current Supply of Surgical Masks', 'One-Week Supply of Surgical Masks',
'Any Current Supply of Eye Protection', 'One-Week Supply of Eye Protection',
'Any Current Supply of Gowns', 'One-Week Supply of Gowns',
'Any Current Supply of Gloves', 'One-Week Supply of Gloves',
'Any Current Supply of Hand Sanitizer', 'One-Week Supply of Hand Sanitizer',
'Ventilator Dependent Unit', 'Number of Ventilators in Facility',
'Number of Ventilators in Use for COVID-19', 'Any Current Supply of Ventilator Supplies',
'One-Week Supply of Ventilator Supplies', 'Total Resident Confirmed COVID-19 Cases Per 1,000 Residents',
'Total Resident COVID-19 Deaths Per 1,000 Residents',
'Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases']
# Count unique values in each categorical column
unique_value_counts = {}
for column in categorical_columns:
unique_values = df2[column].nunique()
unique_value_counts[column] = unique_values
# Print the counts of unique values for each categorical column
print("Categorical Cardinality (Unique Value Counts):")
for column, count in unique_value_counts.items():
print(f"{column}: {count}")
# Plot the counts of unique values as a horizontal bar plot with red color
plt.figure(figsize=(12, 10))
plt.barh(list(unique_value_counts.keys()), unique_value_counts.values(), color='red') # Set color to red
plt.title("Categorical Cardinality (Unique Value Counts)")
plt.xlabel("Count of Unique Values")
plt.ylabel("Categorical Columns")
# Show the plot
plt.tight_layout()
plt.show()
Categorical Cardinality (Unique Value Counts): Week Ending: 3 Federal Provider Number: 15423 Provider Name: 15144 Provider Address: 15386 Provider City: 5256 Provider State: 51 Provider Zip Code: 9226 Submitted Data: 2 Passed Quality Assurance Check: 2 Residents Weekly Admissions COVID-19: 116 Residents Total Admissions COVID-19: 141 Residents Weekly Confirmed COVID-19: 146 Residents Total Confirmed COVID-19: 181 Residents Weekly Suspected COVID-19: 160 Residents Total Suspected COVID-19: 203 Residents Weekly All Deaths: 116 Residents Total All Deaths: 153 Residents Weekly COVID-19 Deaths: 69 Residents Total COVID-19 Deaths: 88 Number of All Beds: 392 Total Number of Occupied Beds: 421 Resident Access to Testing in Facility: 2 Laboratory Type Is State Health Dept: 2 Laboratory Type Is Private Lab: 2 Laboratory Type Is Other: 2 Staff Weekly Confirmed COVID-19: 100 Staff Total Confirmed COVID-19: 139 Staff Weekly Suspected COVID-19: 135 Staff Total Suspected COVID-19: 175 Staff Weekly COVID-19 Deaths: 8 Staff Total COVID-19 Deaths: 10 Shortage of Nursing Staff: 2 Shortage of Clinical Staff: 2 Shortage of Aides: 2 Shortage of Other Staff: 2 Any Current Supply of N95 Masks: 2 One-Week Supply of N95 Masks: 2 Any Current Supply of Surgical Masks: 2 One-Week Supply of Surgical Masks: 2 Any Current Supply of Eye Protection: 2 One-Week Supply of Eye Protection: 2 Any Current Supply of Gowns: 2 One-Week Supply of Gowns: 2 Any Current Supply of Gloves: 2 One-Week Supply of Gloves: 2 Any Current Supply of Hand Sanitizer: 2 One-Week Supply of Hand Sanitizer: 2 Ventilator Dependent Unit: 2 Number of Ventilators in Facility: 69 Number of Ventilators in Use for COVID-19: 44 Any Current Supply of Ventilator Supplies: 2 One-Week Supply of Ventilator Supplies: 2 Total Resident Confirmed COVID-19 Cases Per 1,000 Residents: 3783 Total Resident COVID-19 Deaths Per 1,000 Residents: 1851 Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases: 763
Quality Score for Categorical Columns
import pandas as pd
import matplotlib.pyplot as plt
# Specify the categorical columns for which you want to count unique values
categorical_columns = ['Week Ending', 'Federal Provider Number', 'Provider Name', 'Provider Address',
'Provider City', 'Provider State', 'Provider Zip Code', 'Submitted Data',
'Passed Quality Assurance Check', 'Residents Weekly Admissions COVID-19',
'Residents Total Admissions COVID-19', 'Residents Weekly Confirmed COVID-19',
'Residents Total Confirmed COVID-19', 'Residents Weekly Suspected COVID-19',
'Residents Total Suspected COVID-19', 'Residents Weekly All Deaths',
'Residents Total All Deaths', 'Residents Weekly COVID-19 Deaths',
'Residents Total COVID-19 Deaths', 'Number of All Beds', 'Total Number of Occupied Beds',
'Resident Access to Testing in Facility', 'Laboratory Type Is State Health Dept',
'Laboratory Type Is Private Lab', 'Laboratory Type Is Other', 'Staff Weekly Confirmed COVID-19',
'Staff Total Confirmed COVID-19', 'Staff Weekly Suspected COVID-19', 'Staff Total Suspected COVID-19',
'Staff Weekly COVID-19 Deaths', 'Staff Total COVID-19 Deaths', 'Shortage of Nursing Staff',
'Shortage of Clinical Staff', 'Shortage of Aides', 'Shortage of Other Staff',
'Any Current Supply of N95 Masks', 'One-Week Supply of N95 Masks',
'Any Current Supply of Surgical Masks', 'One-Week Supply of Surgical Masks',
'Any Current Supply of Eye Protection', 'One-Week Supply of Eye Protection',
'Any Current Supply of Gowns', 'One-Week Supply of Gowns',
'Any Current Supply of Gloves', 'One-Week Supply of Gloves',
'Any Current Supply of Hand Sanitizer', 'One-Week Supply of Hand Sanitizer',
'Ventilator Dependent Unit', 'Number of Ventilators in Facility',
'Number of Ventilators in Use for COVID-19', 'Any Current Supply of Ventilator Supplies',
'One-Week Supply of Ventilator Supplies', 'Total Resident Confirmed COVID-19 Cases Per 1,000 Residents',
'Total Resident COVID-19 Deaths Per 1,000 Residents',
'Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases']
# Count unique values in each categorical column
unique_value_counts = {}
for column in categorical_columns:
unique_values = df2[column].nunique()
unique_value_counts[column] = unique_values
# Calculate the quality score for categorical columns
max_score = 100 # Maximum score
# Calculate the overall quality score as the average of the unique value counts
quality_score_categorical = max_score - (sum(unique_value_counts.values()) / (len(categorical_columns) * max_score))
print(f'Quality Score for Categorical Columns: {quality_score_categorical:.2f}')
Quality Score for Categorical Columns: 87.31
Anomaly Detection for Covid 19 Dataset
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# Specify the column for which you want to detect anomalies
column_name = 'Total_Hosp' # Replace with the column you want to analyze
# Calculate the Z-score for the selected column
z_scores = np.abs((df1[column_name] - df1[column_name].mean()) / df1[column_name].std())
# Define a threshold for anomaly detection (you can adjust this threshold)
threshold = 2.0
# Identify anomalies by comparing Z-scores to the threshold
anomalies = df1[z_scores > threshold]
# Plot the data and mark anomalies with different colors
plt.figure(figsize=(12, 6))
plt.plot(df1.index, df1[column_name], label='Data', marker='o', linestyle='-')
plt.scatter(anomalies.index, anomalies[column_name], c='red', marker='x', s=100, label='Anomalies')
plt.title(f"Anomaly Detection for {column_name}")
plt.xlabel("Data Point Index")
plt.ylabel(column_name)
plt.legend()
# Show the plot
plt.tight_layout()
plt.show()
# Print the identified anomalies
print("Identified Anomalies:")
print(anomalies)
Identified Anomalies:
Year Month Bene_Geo_Desc Bene_Mdcd_Mdcr_Enrl_Stus \
0 2020 Overall National All
1 2020 Overall National All
2 2020 Overall National All
4 2020 Overall National All
5 2020 Overall National All
... ... ... ... ...
39060 2022 Second half National All
39062 2022 Second half National All
39063 2022 Second half National Medicare & Medicaid
39064 2022 Second half National Medicare Only
39069 2022 Second half National All
Bene_Race_Desc Bene_Sex_Desc Bene_Mdcr_Entlmt_Stus Bene_Age_Desc \
0 All All All All
1 All All All All
2 All All All All
4 All All All 0-64
5 All All All 65-74
... ... ... ... ...
39060 Non-Hispanic White All All All
39062 All All All All
39063 All All All All
39064 All All All All
39069 Non-Hispanic White All All All
Bene_RUCA_Desc Total_Hosp Total_Enrl Total_Hosp_Per100K Avg_LOS \
0 All 986377.0 6.251189e+07 1577.9030 10.6439
1 Rural 189268.0 1.206914e+07 1568.1985 9.7550
2 Urban 794694.0 4.981956e+07 1595.1445 10.8475
4 All 130536.0 8.319817e+06 1568.9768 11.7848
5 All 342290.0 3.103836e+07 1102.7966 11.1948
... ... ... ... ... ...
39060 Rural 83589.0 6.417565e+07 130.2503 7.9403
39062 Urban 445779.0 3.118962e+08 142.9255 8.8026
39063 Urban 141815.0 5.948475e+07 238.4056 9.8750
39064 Urban 303964.0 2.524114e+08 120.4240 8.3022
39069 Urban 315756.0 2.169459e+08 145.5460 8.5315
Pct_Dschrg_SNF Pct_Dschrg_Expired Pct_Dschrg_Home Pct_Dschrg_Hspc \
0 0.1922 0.1804 0.3577 0.0492
1 0.1738 0.1758 0.3945 0.0391
2 0.1965 0.1814 0.3489 0.0516
4 0.1471 0.1198 0.4958 0.0146
5 0.1450 0.1592 0.4466 0.0243
... ... ... ... ...
39060 0.2120 0.0603 0.4390 0.0373
39062 0.2225 0.0572 0.3935 0.0420
39063 0.2554 0.0535 0.3930 0.0351
39064 0.2072 0.0589 0.3936 0.0452
39069 0.2363 0.0557 0.3747 0.0451
Pct_Dschrg_HomeHealth Pct_Dschrg_Other
0 0.1527 0.0679
1 0.1439 0.0729
2 0.1549 0.0666
4 0.1302 0.0926
5 0.1501 0.0748
... ... ...
39060 0.1820 0.0695
39062 0.2151 0.0697
39063 0.1893 0.0737
39064 0.2272 0.0679
39069 0.2179 0.0703
[556 rows x 19 columns]
Quality Score for Anamoly Detection
# Specify the column for which you want to detect anomalies
column_name = 'Total_Hosp' # Replace with the column you want to analyze
# Calculate the Z-score for the selected column
z_scores = np.abs((df1[column_name] - df1[column_name].mean()) / df1[column_name].std())
# Define a threshold for anomaly detection (you can adjust this threshold)
threshold = 2.0
# Identify anomalies by comparing Z-scores to the threshold
anomalies = df1[z_scores > threshold]
# Calculate the quality score based on the number of anomalies
max_score = 100 # Maximum score
# Calculate the overall quality score based on the number of anomalies
quality_score_anomaly = max_score - (len(anomalies) / len(df1) * max_score)
print(f'Quality Score for Anomaly Detection: {quality_score_anomaly:.2f}')
Quality Score for Anomaly Detection: 98.98
Anomaly Detection for Faclevel Dataset
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# Specify the column for which you want to detect anomalies
column_name = 'Residents Weekly Admissions COVID-19' # Replace with the column you want to analyze
# Calculate the Z-score for the selected column
z_scores = np.abs((df2[column_name] - df2[column_name].mean()) / df2[column_name].std())
# Define a threshold for anomaly detection (you can adjust this threshold)
threshold = 2.0
# Identify anomalies by comparing Z-scores to the threshold
anomalies = df2[z_scores > threshold]
# Plot the data and mark anomalies with different colors
plt.figure(figsize=(12, 6))
plt.plot(df2.index, df2[column_name], label='Data', marker='o', linestyle='-')
plt.scatter(anomalies.index, anomalies[column_name], c='red', marker='x', s=100, label='Anomalies')
plt.title(f"Anomaly Detection for {column_name}")
plt.xlabel("Data Point Index")
plt.ylabel(column_name)
plt.legend()
# Show the plot
plt.tight_layout()
plt.show()
# Print the identified anomalies
print("Identified Anomalies:")
print(anomalies)
Identified Anomalies:
Week Ending Federal Provider Number \
120 05/24/20 015100
333 05/24/20 015192
543 05/24/20 015411
768 05/24/20 035071
827 06/07/20 035099
... ... ...
44721 05/31/20 675896
45403 05/24/20 676179
45607 05/24/20 676255
46048 05/24/20 676416
46170 06/07/20 676456
Provider Name Provider Address \
120 CROWNE HEALTH CARE OF MOBILE 954 NAVCO ROAD
333 ARBOR SPRINGS HEALTH AND REHAB CENTER, LTD 1910 PEPPERELL PKWY
543 SHELBY RIDGE NURSING HOME 881 3RD STREET NORTHEAST
768 MISSION PALMS POST ACUTE 6461 EAST BAYWOOD AVENUE
827 SAPPHIRE OF TUCSON NURSING AND REHAB, LLC 2900 EAST MILBER STREET
... ... ...
44721 RIVER CITY CARE CENTER 921 NOLAN ST
45403 MIDLAND MEDICAL LODGE 3000 MOCKINGBIRD LN
45607 FORT WORTH TRANSITIONAL CARE CENTER 850 12TH AVENUE
46048 BRIGHTPOINTE AT LYTLE LAKE 1201 CLARKS DR
46170 THE MEDICAL RESORT AT WOODLANDS 24854 CATHEDRAL LAKES PKWY
Provider City Provider State Provider Zip Code Submitted Data \
120 MOBILE AL 36605.0 Y
333 OPELIKA AL 36801.0 Y
543 ALABASTER AL 35007.0 Y
768 MESA AZ 85206.0 Y
827 TUCSON AZ 85714.0 Y
... ... ... ... ...
44721 SAN ANTONIO TX 78202.0 Y
45403 MIDLAND TX 79705.0 Y
45607 FORT WORTH TX 76104.0 Y
46048 ABILENE TX 79602.0 Y
46170 SPRING TX 77386.0 Y
Passed Quality Assurance Check Residents Weekly Admissions COVID-19 \
120 Y 13.0
333 Y 41.0
543 Y 14.0
768 Y 37.0
827 Y 28.0
... ... ...
44721 Y 17.0
45403 Y 25.0
45607 Y 16.0
46048 Y 103.0
46170 Y 25.0
... Any Current Supply of Hand Sanitizer \
120 ... Y
333 ... Y
543 ... Y
768 ... Y
827 ... Y
... ... ...
44721 ... Y
45403 ... Y
45607 ... Y
46048 ... Y
46170 ... Y
One-Week Supply of Hand Sanitizer Ventilator Dependent Unit \
120 Y N
333 Y N
543 Y N
768 Y N
827 Y N
... ... ...
44721 Y N
45403 Y N
45607 Y N
46048 Y N
46170 Y Y
Number of Ventilators in Facility \
120 NaN
333 NaN
543 NaN
768 NaN
827 NaN
... ...
44721 NaN
45403 NaN
45607 NaN
46048 NaN
46170 0.0
Number of Ventilators in Use for COVID-19 \
120 NaN
333 NaN
543 NaN
768 NaN
827 NaN
... ...
44721 NaN
45403 NaN
45607 NaN
46048 NaN
46170 0.0
Any Current Supply of Ventilator Supplies \
120 NaN
333 NaN
543 NaN
768 NaN
827 NaN
... ...
44721 NaN
45403 NaN
45607 NaN
46048 NaN
46170 N
One-Week Supply of Ventilator Supplies \
120 NaN
333 NaN
543 NaN
768 NaN
827 NaN
... ...
44721 NaN
45403 NaN
45607 NaN
46048 NaN
46170 N
Total Resident Confirmed COVID-19 Cases Per 1,000 Residents \
120 10.1
333 673.3
543 154.5
768 0.0
827 946.9
... ...
44721 0.0
45403 54.5
45607 640.6
46048 14.3
46170 1000.0
Total Resident COVID-19 Deaths Per 1,000 Residents \
120 50.5
333 198.0
543 27.3
768 64.2
827 265.5
... ...
44721 0.0
45403 0.0
45607 62.5
46048 0.0
46170 0.0
Total Residents COVID-19 Deaths as a Percentage of Confirmed COVID-19 Cases
120 500.0
333 29.4
543 17.6
768 NaN
827 28.0
... ...
44721 NaN
45403 0.0
45607 9.8
46048 0.0
46170 0.0
[706 rows x 55 columns]
Quality score for anamoly detection
# Specify the column for which you want to detect anomalies
column_name = 'Residents Weekly Admissions COVID-19' # Replace with the column you want to analyze
# Calculate the Z-score for the selected column
z_scores = np.abs((df2[column_name] - df2[column_name].mean()) / df2[column_name].std())
# Define a threshold for anomaly detection (you can adjust this threshold)
threshold = 2.0
# Identify anomalies by comparing Z-scores to the threshold
anomalies = df2[z_scores > threshold]
# Calculate the quality score based on the number of anomalies
max_score = 100 # Maximum score
# Calculate the overall quality score based on the number of anomalies
quality_score_anomaly = max_score - (len(anomalies) / len(df2) * max_score)
print(f'Quality Score for {column_name} based on Anomaly Detection: {quality_score_anomaly:.2f}')
Quality Score for Residents Weekly Admissions COVID-19 based on Anomaly Detection: 98.47
QUALITY SCORE FOR THESE FEATURE EXTRACTION IN HELTHCARE DATA
import matplotlib.pyplot as plt
data_completeness_scores_1 = 88.5
data_consistency_scores_1 = 94.2
outliers_scores_1 = 95.3
statistical_summary_scores_1 = 89.9
categorical_cardinality_scores_1 = 99.8
anomaly_detection_scores_1 = 98.9
# Create a subplot for Dataset 1
fig, ax = plt.subplots(figsize=(10, 6))
# Labels and scores for Dataset 1
labels = ['Data Completeness', 'Data Consistency', 'Outliers', 'Statistical Summary', 'Categorical Cardinality', 'Anomaly Detection']
scores = [data_completeness_scores_1, data_consistency_scores_1, outliers_scores_1,
statistical_summary_scores_1, categorical_cardinality_scores_1, anomaly_detection_scores_1]
# Bar colors
colors = ['red', 'blue', 'green', 'yellow', 'pink', 'skyblue']
# Annotate percentages on top of the bars
for label, score, color in zip(labels, scores, colors):
bar = ax.bar(label, score, color=color)
height = bar[0].get_height()
ax.annotate(f'{score:.2f}%', xy=(bar[0].get_x() + bar[0].get_width() / 2, height), xytext=(0, 3),
textcoords="offset points", ha='center', va='bottom', fontsize=10)
ax.set_title('Quality Scores for COVID-19 Dataset')
ax.set_ylabel('Quality Score')
ax.set_ylim(0, 110) # Adjust the y-axis limit as needed
ax.set_xticklabels(labels, rotation=45, ha="right")
plt.tight_layout()
plt.show()
<ipython-input-36-b428aa34f480>:32: UserWarning: FixedFormatter should only be used together with FixedLocator ax.set_xticklabels(labels, rotation=45, ha="right")
import matplotlib.pyplot as plt
data_completeness_scores_1 = 85.2
data_consistency_scores_1 = 100
outliers_scores_1 = 86.1
statistical_summary_scores_1 = 87.8
categorical_cardinality_scores_1 = 87.3
anomaly_detection_scores_1 = 98.4
# Create a subplot for Dataset 1
fig, ax = plt.subplots(figsize=(10, 6))
# Labels and scores for Dataset 1
labels = ['Data Completeness', 'Data Consistency', 'Outliers', 'Statistical Summary', 'Categorical Cardinality', 'Anomaly Detection']
scores = [data_completeness_scores_1, data_consistency_scores_1, outliers_scores_1,
statistical_summary_scores_1, categorical_cardinality_scores_1, anomaly_detection_scores_1]
# Bar colors
colors = ['red', 'blue', 'green', 'yellow', 'pink', 'skyblue']
# Annotate percentages on top of the bars
for label, score, color in zip(labels, scores, colors):
bar = ax.bar(label, score, color=color)
height = bar[0].get_height()
ax.annotate(f'{score:.2f}%', xy=(bar[0].get_x() + bar[0].get_width() / 2, height), xytext=(0, 3),
textcoords="offset points", ha='center', va='bottom', fontsize=10)
ax.set_title('Quality Scores for COVID-19 Dataset')
ax.set_ylabel('Quality Score')
ax.set_ylim(0, 110) # Adjust the y-axis limit as needed
ax.set_xticklabels(labels, rotation=45, ha="right")
plt.tight_layout()
plt.show()
<ipython-input-37-c7f60dd0342e>:32: UserWarning: FixedFormatter should only be used together with FixedLocator ax.set_xticklabels(labels, rotation=45, ha="right")
!pip install nbconvert
Requirement already satisfied: nbconvert in /usr/local/lib/python3.10/dist-packages (6.5.4) Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from nbconvert) (4.9.3) Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (4.11.2) Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from nbconvert) (6.1.0) Requirement already satisfied: defusedxml in /usr/local/lib/python3.10/dist-packages (from nbconvert) (0.7.1) Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (0.4) Requirement already satisfied: jinja2>=3.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (3.1.2) Requirement already satisfied: jupyter-core>=4.7 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (5.4.0) Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.10/dist-packages (from nbconvert) (0.2.2) Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (2.1.3) Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (0.8.4) Requirement already satisfied: nbclient>=0.5.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (0.8.0) Requirement already satisfied: nbformat>=5.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (5.9.2) Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from nbconvert) (23.2) Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (1.5.0) Requirement already satisfied: pygments>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (2.16.1) Requirement already satisfied: tinycss2 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (1.2.1) Requirement already satisfied: traitlets>=5.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert) (5.7.1) Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.10/dist-packages (from jupyter-core>=4.7->nbconvert) (3.11.0) Requirement already satisfied: jupyter-client>=6.1.12 in /usr/local/lib/python3.10/dist-packages (from nbclient>=0.5.0->nbconvert) (6.1.12) Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.10/dist-packages (from nbformat>=5.1->nbconvert) (2.18.1) Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.10/dist-packages (from nbformat>=5.1->nbconvert) (4.19.1) Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->nbconvert) (2.5) Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert) (1.16.0) Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert) (0.5.1) Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert) (23.1.0) Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert) (2023.7.1) Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert) (0.30.2) Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert) (0.10.4) Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.10/dist-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (23.2.1) Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.10/dist-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (2.8.2) Requirement already satisfied: tornado>=4.1 in /usr/local/lib/python3.10/dist-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (6.3.2)
from nbconvert import HTMLExporter
import codecs
# Replace 'your_notebook.ipynb' with the name of your notebook file in Colab
notebook_filename = 'FE_Healthcare.ipynb'
# Create an HTML exporter instance
html_exporter = HTMLExporter()
# Read the notebook file
with codecs.open(notebook_filename, 'r', 'utf-8') as notebook_file:
notebook_content = notebook_file.read()
# Convert the notebook to HTML format
(body, resources) = html_exporter.from_notebook_node(notebook_content)
# Save the HTML content to a file
with open('FE_Healthcare.html', 'w', encoding='utf-8') as html_file:
html_file.write(body)
# Download the HTML file to your local machine
from google.colab import files
files.download('FE_Healthcare.html')
--------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) <ipython-input-12-4e4241a43a2e> in <cell line: 11>() 9 10 # Read the notebook file ---> 11 with codecs.open(notebook_filename, 'r', 'utf-8') as notebook_file: 12 notebook_content = notebook_file.read() 13 # Convert the notebook to HTML format /usr/lib/python3.10/codecs.py in open(filename, mode, encoding, errors, buffering) 904 # Force opening of the file in binary mode 905 mode = mode + 'b' --> 906 file = builtins.open(filename, mode, buffering) 907 if encoding is None: 908 return file FileNotFoundError: [Errno 2] No such file or directory: 'FE_Healthcare.ipynb'